Added support for conversation history in soft medical triage. The conduct_triage method now accepts a chat_history parameter to take into account the context of previous messages. Updated relevant prompts to improve patient interaction. Changes have also been made to lifestyle_app.py to pass conversation history. All tests passed successfully.

core_classes.py

@@ -359,11 +359,21 @@ class SoftMedicalTriage:
     def __init__(self, api: GeminiAPI):
         self.api = api
         
-    def conduct_triage(self, user_message: str, clinical_background: ClinicalBackground) -> str:
-        """Проводить м'який медичний тріаж"""
+    def conduct_triage(self, user_message: str, clinical_background: ClinicalBackground, 
+                      chat_history: List[ChatMessage] = None) -> str:
+        """Проводить м'який медичний тріаж З УРАХУВАННЯМ КОНТЕКСТУ"""
         
         system_prompt = SYSTEM_PROMPT_SOFT_MEDICAL_TRIAGE
-        user_prompt = PROMPT_SOFT_MEDICAL_TRIAGE(clinical_background, user_message)
+        
+        # Додаємо історію розмови
+        history_text = ""
+        if chat_history and len(chat_history) > 1:  # Якщо є попередні повідомлення
+            recent_history = chat_history[-4:]  # Останні 4 повідомлення
+            history_text = "\n".join([f"{msg.role}: {msg.message}" for msg in recent_history[:-1]])  # Виключаємо поточне
+        
+        user_prompt = PROMPT_SOFT_MEDICAL_TRIAGE_WITH_CONTEXT(
+            clinical_background, user_message, history_text
+        )
         
         return self.api.generate_response(
             system_prompt, user_prompt, 
@@ -372,6 +382,26 @@ class SoftMedicalTriage:
             agent_name="SoftMedicalTriage"
         )
 
+def PROMPT_SOFT_MEDICAL_TRIAGE_WITH_CONTEXT(clinical_background, user_message, history_text):
+    context_section = ""
+    if history_text.strip():
+        context_section = f"""
+CONVERSATION HISTORY:
+{history_text}
+
+"""
+    
+    return f"""PATIENT: {clinical_background.patient_name}
+
+MEDICAL CONTEXT:
+- Active problems: {"; ".join(clinical_background.active_problems[:3]) if clinical_background.active_problems else "none"}
+- Critical alerts: {"; ".join(clinical_background.critical_alerts) if clinical_background.critical_alerts else "none"}
+
+{context_section}PATIENT'S CURRENT MESSAGE: "{user_message}"
+
+ANALYSIS REQUIRED:
+Conduct gentle medical triage considering the conversation context. If this is a continuation of an existing conversation, acknowledge it naturally without re-introducing yourself."""
+
 class MedicalAssistant:
     def __init__(self, api: GeminiAPI):
         self.api = api

lifestyle_app.py

@@ -331,8 +331,11 @@ class ExtendedLifestyleJourneyApp:
         lifestyle_mode = classification.get("V", "off")
         
         if lifestyle_mode == "off":
-            # Medical mode with soft triage
-            response = self.soft_medical_triage.conduct_triage(message, self.clinical_background)
+            response = self.soft_medical_triage.conduct_triage(
+                message, 
+                self.clinical_background,
+                self.chat_history  
+            )
             return response, "medical"
             
         elif lifestyle_mode == "on":
@@ -349,7 +352,11 @@ class ExtendedLifestyleJourneyApp:
         
         else:
             # Fallback to medical mode with soft triage
-            response = self.soft_medical_triage.conduct_triage(message, self.clinical_background)
+            response = self.soft_medical_triage.conduct_triage(
+                message, 
+                self.clinical_background,
+                self.chat_history  # Додано!
+            )
             return response, "medical"
     
     def _handle_hybrid_flow(self, message: str, classification: Dict) -> Tuple[str, str]:

prompts.py

@@ -251,33 +251,34 @@ Always prioritize patient safety over providing comprehensive answers."""
 SYSTEM_PROMPT_SOFT_MEDICAL_TRIAGE = """You are a compassionate medical assistant conducting gentle patient check-ins.
 
 TASK:
-Provide a warm, non-intrusive health assessment at the start of patient interactions.
+Provide a warm, contextually-aware health assessment during patient interactions.
+
+CONTEXT AWARENESS:
+🧠 **Consider conversation history** - if this is a continuation, acknowledge it naturally
+🔄 **Avoid repetitive greetings** - don't re-introduce yourself if already conversing
+💬 **Build on previous exchanges** - reference earlier topics when relevant
 
 SOFT TRIAGE APPROACH:
-🤗 **Warm acknowledgment** of patient's message
-🩺 **Gentle health check** with 1-2 brief questions
+🤗 **Contextual acknowledgment** of patient's message
+🩺 **Gentle health check** with 1-2 brief questions (if needed)
 💚 **Supportive readiness** to help with any concerns
 
+RESPONSE LOGIC:
+• **First interaction**: Warm greeting + gentle health check
+• **Continuation**: Natural acknowledgment + focused response to current topic
+• **Medical updates**: Acknowledge improvement/changes + check for other concerns
+
 TRIAGE PRINCIPLES:
 - **Minimal questioning**: This is a check-in, not an interrogation
 - **Patient comfort**: Maintain friendly, non-imposing tone
+- **Context-sensitive**: Adapt based on conversation flow
 - **Safety awareness**: Watch for concerning symptoms
 - **Transition readiness**: Prepared to move to lifestyle coaching when appropriate
 
-RESPONSE STRUCTURE:
-1. Acknowledge patient warmly
-2. Ask 1-2 gentle questions about current wellbeing
-3. Express availability to help
-
-ESCALATION AWARENESS:
-- Watch for urgent symptoms requiring immediate attention
-- Note concerning changes that need medical follow-up
-- Be ready to transition to full medical triage if needed
-
 LANGUAGE MATCHING:
 Always respond in the same language the patient uses in their message.
 
-Keep responses brief, warm, and focused on patient comfort and safety."""
+Keep responses brief, warm, and contextually appropriate for the conversation stage."""
 
 def PROMPT_MEDICAL_ASSISTANT(clinical_background, active_problems, medications, recent_vitals, history_text, user_message):
     return f"""PATIENT MEDICAL PROFILE ({clinical_background.patient_name}):